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ductions into the basics of stochastic and Markov processes Besides the theoretical background, many motivating examples are also given in those books Consequently, we limit discussion here to the essentials of Markov processes and refer to the literature for further details Markov processes constitute a special, perhaps the most important, subclass of stochastic processes, while the latter can be considered as a generalization of the concept of random variables In particular, a stochastic process provides a relation between the elements of a possibly infinite family of random variables A series of random experiments can thus be taken into consideration and analyzed as a whole Definition 21 A stochastic process is defined as a family of random variables {Xt : t E T} where each random variable Xt is indexed by parameter t E 57, which is usually called the time parameter if T G IK+ = [0, co).

The most common way is to linearize arrays row-by-row, as shown in the example: var V: array [1.2, 1.2] of integer; Linearization (row-by-row): V[1,1], V[1,2], V[2,1], V[2,2] The only language that contradicts to this rule is FORTRAN whose tradition is to linearize arrays column-by-column The latter speci cs of FORTRAN may cause an issue when passing an array from a module in FORTRAN to a module written in another language, or vice versa When linearizing arrays row-by-row, the last index is changing faster To represent and address arrays, it is convenient to de ne and calculate, for each k from 1 to n (where n is the number of array dimensions), the dk distance between the elements whose kth indexes are differed by 1, and the rest of the indexes are the same.

The formulas to calculate the size of the array, the distances dk, and the address of an indexed variable are given below: var V: array [l1.u1 , ln.un] of T; sizek = uk lk + 1 dn = size(T); dk = dk+1 * sizek+1, k = 1, , n 1 Align (V) = Align (T) Size (V) = size1 * * sizen * Size(T) Addr (V[ii, , in]) = a0 + i1 * d1 + + in * dn where a0 = Addr (V[0, , 0]) initial address (of the zero s element, if any)..

The set of all possible values of Xt (for each t E T) is known as the state space S of the stochastic process If a countable, discrete-parameter set T is encountered, the stochastic process is called a discrete-parameter process and 7 is commonly represented by (a subset of) No = {O,l, }; otherwise we call it a continuous-parameter process The state space of the stochastic process may also be continuous or discrete Generally, we restrict ourselves here to the investigation of discrete state spaces and in that case refer to the stochastic processes as chains, but both continuous- and discrete-parameter processes are considered Continuous-parameter stochastic processes can be probaDefinition 22 bilistically characterized by the joint (cumulative) distribution function (CDF) Fx(s; t) for a given set of random variables {Xt, , Xt, , , Xt,}, parameter vector t = (tl, t2, .

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, tn) E Iw , and state vector s = (si, ~2, , sn) E Iw , where tl < t2 < < t,: Fx(s;t).